Smoking increased risk of cervical cancer, independent of infection with high-risk HPV typesCommentary
Bibliographic record
Abstract
Is smoking an independent risk factor for cervical cancer, after controlling for infection with high-risk types of human papillomavirus (HPV)? ### Design: nested case–control study within a prospective cohort study. ### Setting: 5 population-based serum banks in 4 Nordic countries, consisting of serum samples from >1 million women, most collected during early pregnancy. ### Patients: cases were 588 women diagnosed with invasive cervical cancer after their serum sample had been banked (mean age at diagnosis 34–56 y). Controls were 2861 women who were free of cancer at the time of the corresponding case’s diagnosis, matched to cases (5 per case) by bank, age at sampling, storage time of sample, and county (in Norway).* ### Risk factors: smoking status as assessed by serum cotinine concentration: <20 ng/ml (non-smokers and women passively exposed), 20 to <100 ng/ml (light smokers), and ⩾100 ng/ml (heavy smokers), with adjustment for presence …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".